Artigo:

This paper proposes a new Optimization Algorithm for the k-medoid Clustering Problem. In this problem, given a dataset X with n objects and f attributes and a fixed number of clusters (k), it is necessary to select k objects called medoids. Each medoid creates a new cluster and the remaining (n–k) objects should be placed into nearest of these clusters, according a distance measure. The goal is minimize the sum of distances between each object and the medoid of its group. This work presents a new algorithm that considers concepts of Biased Random-key Genetic Algorithm. Besides, an approach of path-relinking procedure is related. The computational experiments results are presented in the last section. It was used thirty instances, among well-known datasets of the literature and new datasets artificially constructed. The proposed heuristics are compared with five approaches (four algorithms and one exact method) and the presented algorithms are a alternative and effective way to solve the problem.